Mr. Anh Tu Tran Profile

Anh Tu Tran

at Ho Chi Minh City Univ of Technology

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Publications (1)

Proceedings Article | 13 March 2024 Poster + Paper

Scattering suppression of transillumination images using deep learning for 3D reconstruction of light absorbing structures in turbid medium

Hoang Nhut Huynh, Phuong Anh Bui, Phuong Anh Dam, Anh Tu Tran, Trung Nghia Tran

Proceedings Volume 12857, 128570L (2024) https://doi.org/10.1117/12.3004898

KEYWORDS: Scattering, 3D modeling, Deblurring, Turbidity, Point spread functions, Deep learning, Image deconvolution, Deconvolution, Education and training, Data modeling

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Significance: Transillumination imaging is crucial in diverse applications, from biometrics and medical diagnostics to material characterization. The challenge of scattering-induced blurring has fueled continuous research in the development of effective deblurring techniques. This study contributes to the field by introducing and evaluating a scattering deblurring model rooted in deep learning principles, addressing the intricacies of light-absorbing structures within turbid media.

Aim: The primary objective of this study is to evaluate the precision and robustness of the proposed scattering deblurring model in reconstructing three-dimensional complex structures within the scattering medium. Adopting a multidimensional approach, the study integrates deep learning principles to surpass the traditional deblurring method with point-spread function deconvolution, establishing a framework for achieving high-fidelity 3D reconstruction structure combined with the commonly filtered-back projection method.

Approach: Leveraging a diverse dataset of simulation images to expose the model to various scattering structures, the proposed scattering deblurring technique is based on the Fully Convolutional Network, Attention Res-Unet. The evaluation of the model’s performance incorporates critical metrics such as the intersection over union (IoU) and the contrast improvement ratio (CIR).

Results: The study demonstrates the effectiveness of the proposed scattering-deblurring model in mitigating scattering blur. Evaluation metrics, including a maximum IoU of 0.9737 and a CIR of 7, 166, underscore the superior performance of the proposed method compared to the deconvolution method in the entire angular range.

Conclusions: In conclusion, this study underscores the importance of adaptive imaging techniques that address the diverse and complex geometries encountered in biomedical optics. The proposed scattering-deblurring model, anchored in deep learning principles, presents promising results in enhancing the visualization of light-absorbing structures within turbid media.

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